Today there is ample Gen AI brainwashing trying to convince enterprises that they can spin a Gen AI-powered chatbot in minutes. Yes, there is Gen AI infrastructure available as open source or as licensed models. These LLM models such as Anthropic Claude, OpenAI GPT, and Meta’s Llama have given us a robust foundation to develop Generative AI applications for the next decade. But you can’t simply take these out-of-the-box technologies and hope to instantly improve your customer experience.
Remember what you have in hand: An existing chatbot at the top that (kind of) understands your business but can’t generate content on its own. It can only create answers based on predefined rules.
A robust Generative AI foundation at the bottom that can magically generate content but lacks any context of your business (people, process, priorities, risks, etc).
Along with the above, what you need is the enterprise orchestration layer: What you need is something in the middle that can connect your chatbot application with the bottom Gen AI foundation, enabling the chatbot to truly speak the language of your business. This middle layer is what we call the enterprise orchestration layer.
The enterprise orchestration layer binds your chatbots with Gen AI models, powered by seamless AI integration services.
You may have noticed that the enterprise orchestration layer often takes a backseat in most of the Gen AI conversations. Quite understandably, the onus is on helping people understand the benefits of Gen AI-powered applications. But now that the hype is over, and enterprises have begun to adopt Gen AI applications, we have to prioritize talking about this middle layer where most of the (grunt) work is involved.
The Grunt Work – Explained
Let’s take a multinational banking service organization that, on average, attends to a lakh customer inquiries (chats and calls) per month. Let’s say they envision a customer-facing bespoke Generative AI-powered chatbot. Remember, the chatbot must be able to provide rich contextualized answers that ensure timely assistance for their customers. What would the grunt work entail for this scale?
Data collection and analysis:
- Existing chatbot data: First, data pipelines collect data from the existing chatbot system. This data would include customer queries, chatbot responses, and resolution outcomes. After the data has been cleaned and processed, data analysis begins. The banking team manually identifies strengths and weaknesses of the existing chatbot system, and lessons learned are prompted into the development of the new Gen AI-powered chatbot solution. AI integration services play a critical role here, aligning various data sources and systems to optimize the chatbot’s performance and ensure seamless communication.
- Customer Support Data: Secondly, data from other sources (phone calls, emails) must be similarly collected and analyzed to understand the range of customer inquiries, the complexities involved, and typical solutions provided by human agents. The lessons learned would again be fed into the development of the Gen AI chatbot.
Creating an extensive knowledge base: The bank then focuses on creating or enriching the existing knowledge base, which contains detailed information about the products, processes, services, regulations, and risks involved. They employ data pipelines once again to gather and structure the information. The knowledge base makes extensive use of the graph database, which maps the objects in a set of business processes such as users, customers, tasks, documents, goals, and the relationships between them. But again, this knowledge base is dependent on inputs from the subject matter experts, and the lessons learned will be fed into the development of the Gen AI chatbot with the help of AI integration services.
Manual annotation and labeling:
- Labeling accurate answers: This involves looking at the resolutions of customer inquiries and accordingly labeling them as positive or correct answers. These positive answers are fed into the chatbot systems.
- Disambiguation labeling: There would be situations where the chatbot would generate multiple correct answers. In such cases, human experts would have to help chatbots identify the most relevant and helpful answers.
Labeling risky answers: Sufficient guardrails will have to be developed to ensure the app doesn’t provide any financial advice or engage in any sensitive activities.
The manual gruntwork we discussed so far forms only one part of the enterprise orchestration layer.
The other core is automation, which is ideally responsible for optimization, integration, and scaling of the system. As of now, only 11% of applications that have adopted Gen AI have moved from pilot to scale. Remember, true scale is achieved only when your enterprise orchestration layer is automated end-to-end. This means, as much as strenuous manual work is involved at the start of developing the system, the human oversight should be complemented by end-to-end automation of the workflows—rather than just automating individual elements within the workflows.
This end-to-end automation is performed by the following sublayers:
- Data layer that automatically fetches data from various sources such as chatbot interface, internal databases, or external APIs. The collected data is wrangled and delivered to appropriate AI models by leveraging AI integration services.
- API gateways act as a central hub that authenticates users, ensures compliance, logs request and response pairs, and appropriately routes requests to the best models (if your applications are working on multiple foundation models).
- The MLOps platform manages the entire automated workflow, streamlining deployment and resource utilization.
Create Our First Gen AI Application with Trigent’s AI Launchpad
Get the Trigent Advantage in Creating End-to-End Enterprise Orchestration Layer.
With 30 years of expertise in end-to-end software services delivery, Trigent is already at the forefront of building AI applications. Our consulting and solutions expertise helps organizations build ethical, perceptive, and responsible Gen AI applications. With comprehensive AI integration services, we help enterprises streamline their AI journey, automate workflows, and achieve impactful business outcomes. The range of tools we use at every layer helps you achieve a robust technical foundation on top of which you can build not just a Gen AI-powered chatbot but any potential Generative AI solution use cases bound to bring a humongous wealth of business value.
Talk to Trigent’s Gen AI experts.
